Conventional decision support tools for operational problems in the oil and gas industry separate planning and scheduling of activities within a facility into two distinct processes that may be related to each other through one or more databases that share common information or related by shared common information. Namely, a traditional way to interface planning with scheduling is by updating a scheduling database with planning results. The schedule is created based upon planning results. There is little capability to easily identify opportunities associated with changes in the plan or the schedule when changes are made to one of the plan or the schedule. In instances where scheduling difficulties arise either from inadequate planning, or from unforeseeable circumstances, the shared database is updated with scheduling conflicts and planning adjustments are made. However, due to the fact that conventional data aggregation is manual and unstructured, data disaggregation presents a challenge. Therefore, the lack of transparency, which accompanies entering and processing of the data, results in a virtual trial-and-error environment for decision making.
Moreover, when planning and scheduling processes are conducted separately, there is typically a moment in time when one of them is completed for its results and effects to be communicated to the other one. In the conventional art, such temporal delineation is established, for example, by setting a cyclical time period, e.g., one month, when planning is performed and the schedule is updated. However, considering that the operational activities that concern a particular site or a group of sites are ongoing, dividing the planning and scheduling tasks into arbitrary and rigid segments of time causes numerous downsides related to translating the fragmented decision making process into a continuous real-world environment. It is also possible that the planning and scheduling periods may not be aligned.
For instance, when currently available planning tools assemble data from numerous sources (e.g., prices, constraints, unit operating modes), such data is time dependent, because it needs to correspond to the duration of the time period allocated for planning. In case that a duration of an external event or an internal operational event that affects the schedule exceeds the moment designated for planning decisions, such event is intercepted by the arbitrarily set planning layout, and therefore needs to be represented discretely in the scheduling tools. Accordingly, the resulting data must be averaged to represent the time period covered by the planning tools. The above described features of the conventional art render the overall planning and scheduling process overly rigid, labor-intensive and less effective than desired.
Furthermore, conventional decision support tools for planning and scheduling problems in the oil and gas industry have ranged from manual techniques to simulation and/or optimization models as the solution technology. The manual approach, for example, is limited by inadequate processing and analytical capabilities, among other deficiencies. On the other hand, the optimization-based models do not identify the strategy that yields the optimal solution. The lack of understanding limits the effective use of these numerically-based planning and scheduling tools. Similarly, the simulation-based approach is also not strategy-based and frequently relies upon trial and error for purposes of identifying suitable planning and scheduling decisions. The decision makers may run hundreds of cases in order to develop a program that in the end may not meet all of the desired business needs. The simulation approach is rule-based and like the optimization approach does not produce results that are intuitively understood.
In light of the discussed inadequacies of the existing technology, there is a need for a tool that is capable of solving problems that arise from the segregation of planning and scheduling within a facility and that better addresses the continuous nature of the operational activities at a single site or at multiple sites. In addition, it is highly desirable for such tool to indicate intent and strategy behind formulation and adjustment of the operational events, thereby rendering the overall process more transparent and easier to manage. There is a need for an operational programming tool that is capable of optimizing both planning and scheduling operations that can be modified and adjusted on a continuous basis to reflect changes in both the planned operations and scheduled operations.